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/*! \file
  \brief Functor performing linear combination with a maximum operation used by
  epilogues.
*/

#pragma once

#include <cutlass/half.h>
#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cutlass/array.h"
#include "cutlass/functional.h"
#include "cutlass/numeric_conversion.h"
#include "cutlass/epilogue/thread/activation.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace epilogue {
namespace thread {

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Applies a linear combination operator to an array of elements.
///
/// D = alpha * accumulator + beta * source + uniform
///
template <typename ElementCompute_,      ///< Data type returned by this functor
          typename ElementAccumulator_,  ///< Data type of accumulators
          typename ElementSource_,       ///< Data type of source tensor
          typename ElementTensor_,       ///< Data type of additional tensor
          int Count,  ///< Number of elements computed per operation
                      ///< Usually it is 128/sizeof_bits<ElementOutput_>,
                      ///< but we use 64 or 32 sometimes when there are not
                      ///< enough data to store
          FloatRoundStyle Round = FloatRoundStyle::round_to_nearest>
class LinearCombinationDRelu {
public:
    using ElementOutput = ElementSource_;
    using ElementCompute = ElementCompute_;
    using ElementAccumulator = ElementAccumulator_;
    using ElementSource = ElementSource_;
    using ElementTensor = ElementTensor_;

    static int const kCount = Count;

    using FragmentCompute = Array<ElementCompute, kCount>;
    using FragmentAccumulator = Array<ElementAccumulator, kCount>;
    using FragmentSource = Array<ElementSource, kCount>;
    using FragmentTensor = Array<ElementTensor, kCount>;

    static FloatRoundStyle const kRound = Round;

    /// Host-constructable parameters structure
    struct Params {
        ElementCompute alpha;             ///< scales accumulators
        ElementCompute beta;              ///< scales source tensor
        ElementCompute threshold;         ///< minimum value that is output
        ElementCompute const* alpha_ptr;  ///< pointer to accumulator scalar -
                                          ///< if not null, loads it from memory
        ElementCompute const* beta_ptr;   ///< pointer to source scalar - if not
                                          ///< null, loads it from memory
        //
        // Methods
        //

        CUTLASS_HOST_DEVICE
        Params()
                : alpha(ElementCompute(1)),
                  beta(ElementCompute(0)),
                  threshold(ElementCompute(0)),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute alpha, ElementCompute beta,
               ElementCompute threshold = ElementCompute(0))
                : alpha(alpha),
                  beta(beta),
                  threshold(threshold),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute const* alpha_ptr, ElementCompute const* beta_ptr,
               ElementCompute threshold = ElementCompute(0))
                : alpha(0),
                  beta(0),
                  threshold(threshold),
                  alpha_ptr(alpha_ptr),
                  beta_ptr(beta_ptr) {}
    };

private:
    //
    // Data members
    //

    ElementCompute alpha_;
    ElementCompute beta_;
    ElementTensor threshold_;
    bool participates_in_reduction_;

public:
    /// Constructs the function object, possibly loading from pointers in host
    /// memory
    CUTLASS_HOST_DEVICE
    LinearCombinationDRelu(Params const& params) {
        alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
        beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
        threshold_ = ElementTensor(params.threshold);
        participates_in_reduction_ = true;
    }

    /// Returns true if source is needed
    CUTLASS_HOST_DEVICE
    bool is_source_needed() const { return beta_ != ElementCompute(0); }

    /// Returns true if the threadblock computes the reduction
    CUTLASS_HOST_DEVICE
    bool participates_in_reduction() const {
        return participates_in_reduction_;
    }

    /// Functionally required for serial reduction in the epilogue
    CUTLASS_DEVICE
    void set_k_partition(int k_partition, int k_partition_count) {
        if (k_partition) {
            beta_ = ElementCompute(1);
        }

        if (k_partition != k_partition_count - 1) {
            // set to NaN to make ReLU no-op for all except last k partitions
            int64_t allones = -1;
            threshold_ = reinterpret_cast<ElementTensor const&>(allones);
            participates_in_reduction_ = false;
        }
    }

    /// Computes linear scaling: D = alpha * accumulator + beta * source
    CUTLASS_HOST_DEVICE
    FragmentCompute operator()(FragmentAccumulator const& accumulator,
                               FragmentSource const& source,
                               FragmentTensor const& tensor) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementSource, kCount, Round>
                source_converter;
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        FragmentCompute converted_source = source_converter(source);
        FragmentCompute converted_accumulator =
                accumulator_converter(accumulator);

        // Perform binary operations
        FragmentCompute intermediate;

        multiplies<FragmentCompute> mul_add_source;
        multiply_add<FragmentCompute> mul_add_accumulator;

        intermediate =
                mul_add_source(beta_, converted_source);  // X =  beta * C
        intermediate =
                mul_add_accumulator(alpha_, converted_accumulator,
                                    intermediate);  // D = alpha * Accum + X

        // dReLU = (cond ? dy : 0)
        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < kCount; ++i) {
            ElementTensor cond = tensor[i];
            if (cond <= threshold_) {
                intermediate[i] = ElementCompute();
            }
        }

        return intermediate;
    }

    /// Computes linear scaling: D = alpha * accumulator
    CUTLASS_HOST_DEVICE
    FragmentCompute operator()(FragmentAccumulator const& accumulator,
                               FragmentTensor const& tensor) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        FragmentCompute converted_accumulator =
                accumulator_converter(accumulator);

        // Perform binary operations
        FragmentCompute intermediate;

        multiplies<FragmentCompute> mul_accumulator;

        intermediate = mul_accumulator(
                alpha_, converted_accumulator);  // D = alpha * Accum

        // dReLU = (cond ? dy : 0)
        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < kCount; ++i) {
            ElementTensor cond = tensor[i];
            if (cond <= threshold_) {
                intermediate[i] = ElementCompute();
            }
        }

        return intermediate;
    }
};

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Applies a linear combination operator to an array of elements.
///
/// D = alpha * accumulator + beta * source + uniform
///
template <typename ElementCompute_,      ///< Data type returned by this functor
          typename ElementAccumulator_,  ///< Data type of accumulators
          typename ElementSource_,       ///< Data type of source tensor
          int Count,  ///< Number of elements computed per operation
          FloatRoundStyle Round = FloatRoundStyle::round_to_nearest>
class LinearCombinationDReluConditionalBits {
public:
    using ElementOutput = ElementSource_;
    using ElementCompute = ElementCompute_;
    using ElementAccumulator = ElementAccumulator_;
    using ElementSource = ElementSource_;
    using ElementTensor = uint1b_t;

    static bool const kIsHeavy = false;

    static int const kCount = Count;

    using FragmentCompute = Array<ElementCompute, kCount>;
    using FragmentAccumulator = Array<ElementAccumulator, kCount>;
    using FragmentSource = Array<ElementSource, kCount>;
    using FragmentTensor = Array<ElementTensor, kCount>;

    static FloatRoundStyle const kRound = Round;

    /// Host-constructable parameters structure
    struct Params {
        ElementCompute alpha;             ///< scales accumulators
        ElementCompute beta;              ///< scales source tensor
        ElementCompute const* alpha_ptr;  ///< pointer to accumulator scalar -
                                          ///< if not null, loads it from memory
        ElementCompute const* beta_ptr;   ///< pointer to source scalar - if not
                                          ///< null, loads it from memory
        //
        // Methods
        //

        CUTLASS_HOST_DEVICE
        Params()
                : alpha(ElementCompute(1)),
                  beta(ElementCompute(0)),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute alpha, ElementCompute beta)
                : alpha(alpha),
                  beta(beta),
                  alpha_ptr(nullptr),
                  beta_ptr(nullptr) {}

        CUTLASS_HOST_DEVICE
        Params(ElementCompute const* alpha_ptr, ElementCompute const* beta_ptr)
                : alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {}
    };

private:
    //
    // Data members
    //

    ElementCompute alpha_;
    ElementCompute beta_;
    FragmentTensor predicate_mask_;
    bool participates_in_reduction_;

public:
    /// Constructs the function object, possibly loading from pointers in host
    /// memory
    CUTLASS_HOST_DEVICE
    LinearCombinationDReluConditionalBits(Params const& params) {
        alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
        beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
        participates_in_reduction_ = true;
        predicate_mask_.clear();
    }

    /// Returns true if source is needed
    CUTLASS_HOST_DEVICE
    bool is_source_needed() const { return beta_ != ElementCompute(0); }

    /// Returns true if the threadblock computes the reduction
    CUTLASS_HOST_DEVICE
    bool participates_in_reduction() const {
        return participates_in_reduction_;
    }

    /// Functionally required for serial reduction in the epilogue
    CUTLASS_HOST_DEVICE
    void set_k_partition(int k_partition, int k_partition_count) {
        predicate_mask_.clear();

        if (k_partition) {
            beta_ = ElementCompute(1);
        }

        if (k_partition != k_partition_count - 1) {
            // Avoid computing the reduction if this isn't the final Split-K
            // slice
            participates_in_reduction_ = false;

            bit_not<FragmentTensor> not_op;
            predicate_mask_ = not_op(predicate_mask_);
        }
    }

    /// Computes linear scaling: D = alpha * accumulator + beta * source
    CUTLASS_DEVICE
    FragmentCompute operator()(FragmentAccumulator const& accumulator,
                               FragmentSource const& source,
                               FragmentTensor const& tensor) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementSource, kCount, Round>
                source_converter;
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        FragmentCompute converted_source = source_converter(source);
        FragmentCompute converted_accumulator =
                accumulator_converter(accumulator);

        // Perform binary operations
        FragmentCompute intermediate;

        multiplies<FragmentCompute> mul_add_source;
        multiply_add<FragmentCompute> mul_add_accumulator;

        intermediate = mul_add_source(
                beta_, converted_source);  // X =  beta * C + uniform
        intermediate =
                mul_add_accumulator(alpha_, converted_accumulator,
                                    intermediate);  // D = alpha * Accum + X

        bit_or<FragmentTensor> or_op;

        FragmentTensor predicates = or_op(tensor, predicate_mask_);

        // Obtain from packed bits
        bool conditions[kCount];
        UnpackPredicates<kCount> unpack_predicates;

        unpack_predicates(conditions, predicates);

        // dReLU = (cond ? dy : 0)
        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < kCount; ++i) {
            if (!conditions[i]) {
                intermediate[i] = ElementCompute();
            }
        }

        return intermediate;
    }

    /// Computes linear scaling: D = alpha * accumulator
    CUTLASS_HOST_DEVICE
    FragmentCompute operator()(FragmentAccumulator const& accumulator,
                               FragmentTensor const& tensor) const {
        // Convert source to interal compute numeric type
        NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round>
                accumulator_converter;

        FragmentCompute converted_accumulator =
                accumulator_converter(accumulator);

        // Perform binary operations
        FragmentCompute intermediate;

        multiplies<FragmentCompute> mul_accumulator;

        intermediate = mul_accumulator(
                alpha_, converted_accumulator);  // D = alpha * Accum

        bit_or<FragmentTensor> or_op;

        FragmentTensor predicates = or_op(tensor, predicate_mask_);

        // Obtain from packed bits
        bool conditions[kCount];
        UnpackPredicates<kCount> unpack_predicates;

        unpack_predicates(conditions, predicates);

        // dReLU = (cond ? dy : 0)
        CUTLASS_PRAGMA_UNROLL
        for (int i = 0; i < kCount; ++i) {
            if (!conditions[i]) {
                intermediate[i] = ElementCompute();
            }
        }

        return intermediate;
    }
};

/////////////////////////////////////////////////////////////////////////////////////////////////

}  // namespace thread
}  // namespace epilogue
}  // namespace cutlass

/////////////////////////////////////////////////////////////////////////////////////////////////
